502 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 23, NO. 1, JANUARY 2017
possessing programming skills. We demonstrate that DataClips covers
a wide range of data videos contained in our corpus, albeit limiting the
level of customization of the visuals and animations. We also report
on a qualitative user study with 12 participants comparing DataClips
to Adobe Illustrator/After Effects software, commonly used to create
data videos. Non-experts in motion graphics could create a larger
number of data videos than those with expertise using the commercial
tool, and with no loss in data video quality.
To summarize, our contributions are threefold: (1) the DataClips
tool; (2) a library of data-driven clips that can be easily extended with
new clips; and (3) a demonstration showing the ability to create data
clips in an efficient manner.
2 R
ELATED WORK
In this section, we provide an overview of prior research around data
storytelling and narrative visualization, as well as existing solutions
for authoring narrative visualizations and data videos.
2.1 Data Storytelling & Narrative Visualization
The visualization literature is primarily focused on methods for visu-
alizing data to facilitate in depth data analysis and exploration. Ideally,
a data analyst can discover new insights from visualizations to com-
municate or convey a story about the data to its stakeholders. Recently,
there has been an upsurge in work transforming data insights into vis-
ual stories [37], through various perspectives [27][36][38]. A number
of visual analytics systems have also integrated storytelling features in
their design (e.g., in-place annotations [25], exporting selected graph-
ical history states [30]). However, supporting an easy creation of rich
and diverse stories based on data insights is still an unsolved problem.
We are witnessing a growing research interest in studying story-
telling techniques for creating more engaging and compelling data sto-
ries or narrative visualizations. In a design space analysis of 58 narra-
tive visualizations, Segel and Heer [41] characterized data videos un-
der different genres. Film and video-based data stories (i.e. data vid-
eos) are recognized among the seven genres of narrative visualization.
Additional work has focused on specific elements of a narrative visu-
alization. Bateman et al. [20] and Borkin et al. [21] specifically fo-
cused on understanding infographics and what makes them appealing
or memorable to a large audience. By studying an online corpus of
narrative visualizations from the area of journalism, Hullman et al.
[32] identified categories of rhetorical techniques affecting reader in-
terpretation. Researchers have also looked at methods of sequencing
[34] and transitioning [19] elements in a narrative visualization.
Data videos have also been studied from the perspective of film
narratives, a medium that bears significant similarity with data videos.
Amini et al. [18] examined 50 data videos and teased apart the various
dimensions of such a storytelling genre with respect to narratives in
film or cinematography. Their results show that data videos exhibit
clear narrative structures and use various presentation styles for ani-
mating and displaying data. While prior work has shed light on the
possibilities of data videos, their structural constituents, and their use
for mass appeal, there has been little research on enabling people to
create such narrative visualizations.
2.2 Authoring Narrative Visualizations
The widespread adoption of infographics in fields such as data jour-
nalism has motivated researchers to investigate ways for making it
easy to author narrative visualizations. One approach involves auto-
matically generating explanatory visualizations from data
[35][33][26]. This is possible by tailoring a specific data visualization
type or dataset. In addition, the storytelling elements of the generated
narrative visualizations are limited to annotations overlaid on data vis-
ualizations, not allowing for a rich data story.
To support diverse data stories and to lower the barriers for creat-
ing narrative visualizations, Satyanarayan and Heer [40] introduced
Ellipsis based on a set of abstractions for storytelling with data visu-
alizations. The graphical user interface of Ellipsis allows people to im-
port data visualizations and add storytelling elements to create multi-
ple scenes. However, such a tool does not consolidate the various fea-
tures necessary to craft a complete data video, based on the concept of
a data clips—a ubiquitous element of major data videos.
2.3 Video-based Storytelling
Video-based storytelling is an active research topic and such media
are also referred to as “annotated videos” or “multimedia presenta-
tions” [39]. Authoring a video-based story involves developing a nar-
rative using a collection of media assets and added annotations.
Bulterman and Hardman [24] identified the key authoring problems to
address when designing an authoring environment for video-based
stories (e.g., the ease of creating unique videos). We argue that similar
paradigm can be extended and considered for authoring data videos.
Shen et al. [42] have developed a video-based authoring system
that suggests candidates for the “next scene” based on semantic rela-
tionships between scenes. Similarly, video story creation tools for
non-experts, such as iMovie Trailers [8] and Animoto [3], provide
templates that help novices follow a fixed narrative structure and ar-
range captured content. However, these tools rely on people to decide
on their own the appropriate types of elements and how to include
them in their stories. To eliminate this burden from authors (especially
since we are targeting non-expert authors), we built DataClips based
on the concept of predefined story abstractions in our library of data
clips, allowing non-experts to rapidly generate a variety of data stories.
3 D
ATACLIPS
3.1 Motivations
The building blocks of data videos are individual data-driven video
sequences, or data clips, each targeting a specific insight of the story
conveyed by an animated visualization. Many data videos found
online are produced by a dedicated department, e.g., The Guardian
visuals [7], or crafted by an independent company [13]. Through an
informal interview [28] with the directors of the company who has
created several data videos [13] as well as data journalists at the recent
Dagstuhl seminar on data-driven storytelling [16], we learned that one
minute of data video, excluding data analysis and insights extraction,
takes about a week’s worth of work from a scripter and an experienced
motion graphic designer. Iterating over the video material is costly as
each sequence involves several hours of work using Adobe Illustrator
[1] for the visual designs and After Effects [2] for the animations.
Thus, a significant time is spent upfront on scripting and storyboard-
ing; but iteration is often unavoidable as clients have trouble envision-
ing the final product without experiencing earlier versions. As design
and animations are customized, updating a video with new data also
requires a significant amount of time. While these comments may not
be representative of the creation of all existing data videos, they give
an idea of the overhead and skills required for their creation. We aim
at lowering the barriers to authoring data videos, to help a wider audi-
ence to use this storytelling medium.
3.2 Usage Scenarios and Target Audience
We closely engaged with two professionals who communicate stories
supported by data on a regular basis: Kate, an investigative journalist,
and Matt, a finance manager. Both are experts in data analysis but have
no expertise with programming or video editing. We met three times
with them, gathering their usage scenarios, relevant data, and creating
data videos included on our companion website [17].
Rapid video prototyping tool. Kate works for a national news outlet.
Her role is to find data and facts, and analyze them to craft news stories
on a variety of topics. Kate finds data videos and animated visualiza-
tions effective for telling a data story to her TV Channel audience on
its website or on social media. She rarely creates them herself, how-
ever, because they require a substantial amount of time and resources
from a dedicated department in her company. She saw the greatest op-
portunities for an authoring tool to support her (1) to quickly craft
short data videos for informal breaking news to be shared on social
media sites, and (2) as a prototyping tool to experiment with different
narratives and ease communication with her graphics department
when producing a high-end data video.
Data video clips authoring tool. Matt’s role is to report on financial
results and opportunities for a series of products. Matt spends about a
fourth of his time compiling presentations to report to executives in
the company. Matt found short animated visualizations (illustrating a
single insight) the most compelling to “bring dry and static charts to
life” in presentations and reports. He mentioned that dues to time con-
straints, he does not create such animations in Microsoft PowerPoint
or other tools, especially as they are tedious to update (for each quarter
and each product). Figure 1 shows clips created with our tool based on
Matt’s data and insights to support a story of sales and the evolution
of promotional events affecting the sales.
3.3 Design Considerations
Considering motivations and scenarios, we settled on four design con-
siderations (DCs) for an authoring tool for non-programmers and non-
video producers. Our premise is that the author has already collected
and analyzed data to extract a set of insights for the video.
(DC1) Lower the barrier for authoring data videos. We strive to
strike a balance between predefined templates and customizable data-
driven videos. Our target audience includes those who have collected
and explored their data but are unlikely to have the skills or time to
master visual design skills or video editing software. While video tem-
plates (e.g., iMovie Trailers) are easiest to create, they are unlikely to
cover the wide range of stories people can tell with their data [18]. We
propose to rely on a set of templates for short video clips that authors
can populate with their data and sequence together.
(DC2) Emphasize pictographs. The use of pictographs or isotypes is
heavily present in data videos [8]. Such icon-based data visualizations,
reinforces data semantics, may require less interpretation time and in-
crease story retention [20][29]. However, most editing tools [1] today
only support manual graphical creation, leading to inaccurate visual
encodings. Our goal is to support the creation of accurate animated
pictographs by generating them from data.
(DC3) Support data-driven attention cues. Attention cues and strat-
egies are extensively used in data videos to engage viewers and guide
their attention during the delivery of a story [18]. For example, it is
common to progressively disclose annotations while highlighting re-
lated elements within a data visualization. We aim at supporting the
creation of data-driven attention cues, enabling authors to import them
along with the data, rather than adding them manually on a case-by-
case basis. We also propose to include animated transitions between
visualizations [31]. Such animated transitions are uncommon in data
videos today as they are complicated to craft.
(DC4) One-of-a-kind data video. Our first goal is to provide an au-
thoring tool for novices with a reasonable level of customization: to
easily create videos with a different look and feel. For example, rather
than enabling users to select the animation timing and behavior of each
individual element of a visualization (as PowerPoint does), we chose
to enable users to have controlled timing for sets of elements (e.g. axes
and bars in a bar chart). Our architecture is modular: it allows ad-
vanced users, able to produce code, to easily extend the capabilities of
the tool by adding clips.
3.4 User Interface
With the considerations above in mind, we implemented DataClips
(http://hci.cs.umanitoba.ca/projects-and-research/details/dataclips).
Its interface (Figure 2) is composed of three panels:
1. Clip Library, populated with a set of data-driven clips we describe
in detail in section 4;
2. My Clips, a workspace panel where clips are previewed and se-
quenced to form a longer video, and;
3. Clip Configuration panel, where users can assign data to each indi-
vidual clip and customize its visual appearance.
We illustrate the main components and features of DataClips through
the creation of a short video. Let us imagine Emma, InfoVis paper co-
chair this year, who would like to create a short data-driven video to
illustrate statistics on the conference attendance and its evolution over
the past five years. Emma has gathered the data into a spreadsheet and
collected a number of insights to communicate the evolution of the
number and gender of authors over the years.
Fig. 2. Annotated screenshot of DataClips tool interface: a) saved clip sequences, b) clip preview and sequencing panel, c) the clip library panel,
d) clip configuration panel, e) import new data, f) clear all clips in preview/sequencing panel, g) category of clips for filling pictographs, h) data
configuration options and corresponding input boxes, and i) helper images including numbered items corresponding to the input boxes, j) visual
and animation configuration options and corresponding input fields.